Most of today's natural language processing solutions use neural network-based architectures. An important underlying technology in such an architecture is word vectors. A word vector maps a word to a fixed dimension, and the vector represents semantic information of the word.
In the existing technologies, common algorithms for generating word vectors, such as Google's word vector algorithm and Microsoft's deep neural network algorithm, often run on a single computer.
Based on the existing technologies, an efficient large-scale word vector training solutions is needed.